{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_context(\"poster\")\n",
    "sns.set_style(\"ticks\")\n",
    "\n",
    "from collections import Counter, defaultdict\n",
    "from itertools import count, chain\n",
    "import random\n",
    "import math\n",
    "\n",
    "import dynet as dy\n",
    "import numpy as np\n",
    "\n",
    "from tqdm import tnrange, tqdm_notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# format of files: each line is \"word1/tag2 word2/tag2 ...\"\n",
    "train_file=\"data/cleaned/train.BIEOU.tsv\"\n",
    "test_file=\"data/cleaned/dev.BIEOU.tsv\"\n",
    "\n",
    "MAX_LIK_ITERS = 3\n",
    "SMALL_NUMBER = -1e10\n",
    "MARGIN = 0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Vocab:\n",
    "    def __init__(self, w2i=None):\n",
    "        if w2i is None: w2i = defaultdict(count(0).next)\n",
    "        self.w2i = dict(w2i)\n",
    "        self.i2w = {i:w for w,i in w2i.iteritems()}\n",
    "    @classmethod\n",
    "    def from_corpus(cls, corpus):\n",
    "        w2i = defaultdict(count(0).next)\n",
    "        for sent in corpus:\n",
    "            [w2i[word] for word in sent]\n",
    "        return Vocab(w2i)\n",
    "\n",
    "    def size(self): \n",
    "        return len(self.w2i.keys())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def read(fname):\n",
    "    \"\"\"\n",
    "    Read a NER-tagged file where each line is of the form \"word1 tag2\\nword2 tag2 ...\"\n",
    "    Yields lists of the form [(word1,tag1), (word2,tag2), ...]\n",
    "    \"\"\"\n",
    "    with file(fname) as fh:\n",
    "        sent = []\n",
    "        for line in fh:\n",
    "            line = line.strip()\n",
    "            if line == \"\":\n",
    "                if sent:\n",
    "                    yield sent\n",
    "                sent = []\n",
    "                continue\n",
    "            if line:\n",
    "                line = tuple(line.split(\"\\t\"))\n",
    "                sent.append(line)\n",
    "    if sent:\n",
    "        yield sent\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train: 2394, Test: 1000\n"
     ]
    }
   ],
   "source": [
    "train=list(read(train_file))\n",
    "test=list(read(test_file))\n",
    "print(\"Train: %s, Test: %s\" % (len(train), len(test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('@SammieLynnsMom', 'O'), ('@tg10781', 'O'), ('they', 'O'), ('will', 'O'), ('be', 'O'), ('all', 'O'), ('done', 'O'), ('by', 'O'), ('Sunday', 'O'), ('trust', 'O'), ('me', 'O'), ('*wink*', 'O')]\n"
     ]
    }
   ],
   "source": [
    "print(train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "words=[]\n",
    "tags=[]\n",
    "chars=set()\n",
    "wc=Counter()\n",
    "for sent in train:\n",
    "    for w,p in sent:\n",
    "        words.append(w)\n",
    "        tags.append(p)\n",
    "        chars.update(w)\n",
    "        wc[w]+=1\n",
    "words.append(\"_UNK_\")\n",
    "words.append(\"_S_\")\n",
    "tags.append(\"_S_\")\n",
    "chars.add(\"<*>\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vw = Vocab.from_corpus([words]) # TODO Vocab\n",
    "vt = Vocab.from_corpus([tags])\n",
    "vc = Vocab.from_corpus([chars])\n",
    "UNK = vw.w2i[\"_UNK_\"]\n",
    "S_W = vw.w2i[\"_S_\"]\n",
    "S_T = vt.w2i[\"_S_\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nwords = vw.size()\n",
    "ntags  = vt.size()\n",
    "nchars  = vc.size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10588, 42, 93)\n"
     ]
    }
   ],
   "source": [
    "print(nwords, ntags, nchars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# DyNet Starts\n",
    "\n",
    "model = dy.Model()\n",
    "trainer = dy.AdamTrainer(model)\n",
    "\n",
    "WORDS_LOOKUP = model.add_lookup_parameters((nwords, 64))\n",
    "CHARS_LOOKUP = model.add_lookup_parameters((nchars, 20))\n",
    "TRANS_LOOKUP = model.add_lookup_parameters((ntags, ntags))\n",
    "p_t1  = model.add_lookup_parameters((ntags, 30))\n",
    "\n",
    "# MLP on top of biLSTM outputs 100 -> 32 -> ntags\n",
    "pH = model.add_parameters((32, 50*2))\n",
    "pO = model.add_parameters((ntags, 32))\n",
    "\n",
    "# word-level LSTMs\n",
    "fwdRNN = dy.LSTMBuilder(1, 128, 50, model) # layers, in-dim, out-dim, model\n",
    "bwdRNN = dy.LSTMBuilder(1, 128, 50, model)\n",
    "\n",
    "# char-level LSTMs\n",
    "cFwdRNN = dy.LSTMBuilder(1, 20, 32, model)\n",
    "cBwdRNN = dy.LSTMBuilder(1, 20, 32, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def word_rep_backoff(w, cf_init, cb_init):\n",
    "    if wc[w] > 5:\n",
    "        w_index = vw.w2i[w]\n",
    "        return WORDS_LOOKUP[w_index]\n",
    "    else:\n",
    "        pad_char = vc.w2i[\"<*>\"]\n",
    "        char_ids = [pad_char] + [vc.w2i[c] for c in w] + [pad_char]\n",
    "        char_embs = [CHARS_LOOKUP[cid] for cid in char_ids]\n",
    "        fw_exps = cf_init.transduce(char_embs)\n",
    "        bw_exps = cb_init.transduce(reversed(char_embs))\n",
    "        return dy.concatenate([ fw_exps[-1], bw_exps[-1] ])\n",
    "    \n",
    "    \n",
    "def word_rep_all(w, cf_init, cb_init):\n",
    "    if w in wc and wc[w] > 5:\n",
    "        w_index = vw.w2i[w]\n",
    "    else:\n",
    "        w_index = UNK\n",
    "    w_embed = WORDS_LOOKUP[w_index]\n",
    "    ## Add char embeddings\n",
    "    pad_char = vc.w2i[\"<*>\"]\n",
    "    char_ids = [pad_char] + [vc.w2i[c] for c in w] + [pad_char]\n",
    "    char_embs = [CHARS_LOOKUP[cid] for cid in char_ids]\n",
    "    fw_exps = cf_init.transduce(char_embs)\n",
    "    bw_exps = cb_init.transduce(reversed(char_embs))\n",
    "    return dy.concatenate([w_embed, fw_exps[-1], bw_exps[-1]])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def build_tagging_graph(words, word_rep=word_rep_backoff):\n",
    "    dy.renew_cg()\n",
    "    # parameters -> expressions\n",
    "    H = dy.parameter(pH)\n",
    "    O = dy.parameter(pO)\n",
    "\n",
    "    # initialize the RNNs\n",
    "    f_init = fwdRNN.initial_state()\n",
    "    b_init = bwdRNN.initial_state()\n",
    "\n",
    "    cf_init = cFwdRNN.initial_state()\n",
    "    cb_init = cBwdRNN.initial_state()\n",
    "\n",
    "    # get the word vectors. word_rep(...) returns a 128-dim vector expression for each word.\n",
    "    wembs = [word_rep(w, cf_init, cb_init) for w in words]\n",
    "    wembs = [dy.noise(we,0.1) for we in wembs] # optional\n",
    "\n",
    "    # feed word vectors into biLSTM\n",
    "    fw_exps = f_init.transduce(wembs)\n",
    "    bw_exps = b_init.transduce(reversed(wembs))\n",
    "# OR\n",
    "#    fw_exps = []\n",
    "#    s = f_init\n",
    "#    for we in wembs:\n",
    "#        s = s.add_input(we)\n",
    "#        fw_exps.append(s.output())\n",
    "#    bw_exps = []\n",
    "#    s = b_init\n",
    "#    for we in reversed(wembs):\n",
    "#        s = s.add_input(we)\n",
    "#        bw_exps.append(s.output())\n",
    "\n",
    "    # biLSTM states\n",
    "    bi_exps = [dy.concatenate([f,b]) for f,b in zip(fw_exps, reversed(bw_exps))]\n",
    "\n",
    "    # feed each biLSTM state to an MLP\n",
    "    exps = []\n",
    "    for bi in bi_exps:\n",
    "        r_t = O*(dy.tanh(H * bi))\n",
    "        exps.append(r_t)\n",
    "\n",
    "    return exps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def viterbi_decoding(vecs, gold_tags = []):\n",
    "    # Initialize\n",
    "    init_prob = [SMALL_NUMBER] * ntags\n",
    "    init_prob[S_T] = 0\n",
    "    for_expr = dy.inputVector(init_prob)\n",
    "    best_ids = []\n",
    "    trans_exprs = [TRANS_LOOKUP[tid] for tid in range(ntags)]\n",
    "    # Perform the forward pass through the sentence\n",
    "    for i, vec in enumerate(vecs):\n",
    "        my_best_ids = []\n",
    "        my_best_exprs = []\n",
    "        for next_tag in range(ntags):\n",
    "            # Calculate vector for single next tag\n",
    "            next_single_expr = for_expr + trans_exprs[next_tag]\n",
    "            next_single = next_single_expr.npvalue()\n",
    "            # Find and save the best score\n",
    "            my_best_id = np.argmax(next_single)\n",
    "            my_best_ids.append(my_best_id)\n",
    "            my_best_exprs.append(dy.pick(next_single_expr, my_best_id))\n",
    "        # Concatenate the scores for all vectors together\n",
    "        for_expr = dy.concatenate(my_best_exprs) + vec\n",
    "        # Give a bonus to all but the correct tag if using margin\n",
    "        if MARGIN != 0 and len(gold_tags) != 0:\n",
    "            adjust = [MARGIN] * ntags\n",
    "            adjust[vt.w2i[gold_tags[i]]] = 0\n",
    "            for_expr = for_expr + dy.inputVector(adjust)\n",
    "        # Save the best ids\n",
    "        best_ids.append(my_best_ids)\n",
    "    # Perform the final step to the sentence terminal symbol\n",
    "    next_single_expr = for_expr + trans_exprs[S_T]\n",
    "    next_single = next_single_expr.npvalue()\n",
    "    my_best_id = np.argmax(next_single)\n",
    "    best_expr = dy.pick(next_single_expr, my_best_id)\n",
    "    # Perform the reverse pass\n",
    "    best_path = [vt.i2w[my_best_id]]\n",
    "    for my_best_ids in reversed(best_ids):\n",
    "        my_best_id = my_best_ids[my_best_id]\n",
    "        best_path.append(vt.i2w[my_best_id])\n",
    "    best_path.pop() # Remove final <s>\n",
    "    best_path.reverse()\n",
    "    # Return the best path and best score as an expression\n",
    "    return best_path, best_expr\n",
    "\n",
    "def forced_decoding(vecs, tags):\n",
    "    # Initialize\n",
    "    for_expr = dy.scalarInput(0)\n",
    "    for_tag = S_T\n",
    "    # Perform the forward pass through the sentence\n",
    "    for i, vec in enumerate(vecs): \n",
    "        my_tag = vt.w2i[tags[i]]\n",
    "        for_expr = for_expr + dy.pick(TRANS_LOOKUP[my_tag], for_tag) + vec[my_tag]\n",
    "        for_tag = my_tag\n",
    "    for_expr = for_expr + dy.pick(TRANS_LOOKUP[S_T], for_tag)\n",
    "    return for_expr\n",
    "\n",
    "def viterbi_sent_loss(vecs, tags):\n",
    "    #vecs = build_tagging_graph(words)\n",
    "    viterbi_tags, viterbi_score = viterbi_decoding(vecs, tags)\n",
    "    if viterbi_tags != tags:\n",
    "        reference_score = forced_decoding(vecs, tags)\n",
    "        return viterbi_score - reference_score\n",
    "    else:\n",
    "        return dy.scalarInput(0)\n",
    "\n",
    "def sent_loss(vecs, tags):\n",
    "    #vecs = build_tagging_graph(words)\n",
    "    errs = []\n",
    "    for v,t in zip(vecs,tags):\n",
    "        tid = vt.w2i[t]\n",
    "        err = dy.pickneglogsoftmax(v, tid)\n",
    "        errs.append(err)\n",
    "    return dy.esum(errs)\n",
    "\n",
    "def tag_sent(vecs):\n",
    "    #vecs = build_tagging_graph(words)\n",
    "    vecs = [dy.softmax(v) for v in vecs]\n",
    "    probs = [v.npvalue() for v in vecs]\n",
    "    tags = []\n",
    "    for prb in probs:\n",
    "        tag = np.argmax(prb)\n",
    "        tags.append(vt.i2w[tag])\n",
    "    return tags\n",
    "\n",
    "def predict(sent, viterbi=True, word_rep=word_rep_backoff):\n",
    "    words = [w for w,t in sent]\n",
    "    golds = [t for w,t in sent]\n",
    "    vecs = build_tagging_graph(words, word_rep=word_rep)\n",
    "    if viterbi:\n",
    "        tags, loss_exp = viterbi_decoding(vecs)\n",
    "    else:\n",
    "        tags = tag_sent(vecs)\n",
    "    return tags\n",
    "\n",
    "def print_sequences(sequences, predictions, filename, test_data=False, notypes=False):\n",
    "    with open(filename, \"wb+\") as fp:\n",
    "        for seq, pred in zip(sequences, predictions):\n",
    "            for t, p in zip(seq, pred):\n",
    "                token, tag = t\n",
    "                if tag[0] == \"U\":\n",
    "                    tag = \"B%s\" % tag[1:]\n",
    "                if tag[0] == \"E\":\n",
    "                    tag = \"I%s\" % tag[1:]\n",
    "                if p[0] == \"U\":\n",
    "                    p = \"B%s\" % p[1:]\n",
    "                if p[0] == \"E\":\n",
    "                    p = \"I%s\" % p[1:]\n",
    "                if notypes:\n",
    "                    tag = tag[0]\n",
    "                    p = p[0]\n",
    "                if test_data:\n",
    "                    line = \"\\t\".join((token, p))\n",
    "                else:\n",
    "                    line = \"\\t\".join((token, tag, p))\n",
    "                print >> fp, line\n",
    "            print >> fp, \"\"\n",
    "    print \"Done\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "num_tagged = cum_loss = 0\n",
    "token_scores = []\n",
    "sent_scores = []\n",
    "avg_loss = []\n",
    "word_rep=word_rep_all\n",
    "for ITER in tnrange(50, desc=\"Iterations\"):\n",
    "    random.shuffle(train)\n",
    "    for i,s in enumerate(tqdm_notebook(train, desc=\"Training Items\", leave=False),1):\n",
    "        if i % 500 == 0:\n",
    "            trainer.status()\n",
    "            avg_loss.append(cum_loss / num_tagged)\n",
    "            cum_loss = 0\n",
    "            num_tagged = 0\n",
    "        if i % 10000 == 0 or i == len(train)-1: \n",
    "            good_sent = bad_sent = good = bad = 0.0\n",
    "            for sent in tqdm_notebook(test, desc=\"Test instances\", leave=False):\n",
    "                words = [w for w,t in sent]\n",
    "                golds = [t for w,t in sent]\n",
    "                vecs = build_tagging_graph(words, word_rep=word_rep)\n",
    "                if ITER < MAX_LIK_ITERS:\n",
    "                    tags = tag_sent(vecs)\n",
    "                else:\n",
    "                    tags, loss_exp = viterbi_decoding(vecs)\n",
    "                if tags == golds: good_sent += 1\n",
    "                else: bad_sent += 1\n",
    "                for go,gu in zip(golds,tags):\n",
    "                    if go == gu: good += 1\n",
    "                    else: bad += 1\n",
    "            #print good/(good+bad), good_sent/(good_sent+bad_sent)\n",
    "            token_scores.append(good/(good+bad))\n",
    "            sent_scores.append(good_sent/(good_sent+bad_sent))\n",
    "        # train on sent\n",
    "        words = [w for w,t in s]\n",
    "        golds = [t for w,t in s]\n",
    "        vecs = build_tagging_graph(words, word_rep=word_rep)\n",
    "        if ITER < MAX_LIK_ITERS:\n",
    "            loss_exp =  sent_loss(vecs, golds)\n",
    "        else:\n",
    "            loss_exp =  viterbi_sent_loss(vecs, golds)\n",
    "        cum_loss += loss_exp.scalar_value()\n",
    "        num_tagged += len(golds)\n",
    "        loss_exp.backward()\n",
    "        trainer.update()\n",
    "    #print \"epoch %r finished\" % ITER\n",
    "    trainer.update_epoch(1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f66a4443410>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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tWqV7771XpaWll50jIyND99xzj/71r39p5MiR+uEPf6h77rlHxcXFWrZsme69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qA25A6eOyAWBonxxkpdeLB+1bpeHKAnl+z4BkccwI3+MZRphpRuG/BKFIAqk55CRk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\nflg3FccBGZQxQxBEEIQ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f66c90e0f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.subplot2grid((2,2), (0,0), colspan=2)\n",
    "ax.plot(avg_loss)\n",
    "ax.set_ylabel(\"Avg. Loss\")\n",
    "ax = plt.subplot2grid((2,2), (1,0))\n",
    "ax.plot(token_scores)\n",
    "ax.set_ylabel(\"Token Scores\")\n",
    "ax = plt.subplot2grid((2,2), (1,1))\n",
    "ax.plot(sent_scores)\n",
    "ax.set_ylabel(\"Sent Scores\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(('RT', 'O'), 'O'),\n",
       " (('@shashiranjanttv', 'O'), 'O'),\n",
       " ((':', 'O'), 'O'),\n",
       " (('@shashiranjanttv', 'O'), 'O'),\n",
       " (('second', 'O'), 'O'),\n",
       " (('is', 'O'), 'O'),\n",
       " (('Bawana', 'U-geo-loc'), 'U-person'),\n",
       " (('constituency', 'O'), 'O'),\n",
       " (('on', 'O'), 'O'),\n",
       " (('Feb', 'O'), 'O'),\n",
       " (('4', 'O'), 'O'),\n",
       " (('-', 'O'), 'O'),\n",
       " (('Final', 'O'), 'O')]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zip(test[2], predict(test[2], word_rep=word_rep))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done\n"
     ]
    }
   ],
   "source": [
    "print_sequences(test, [predict(s,word_rep=word_rep) for s in test], \"test.dynet.tsv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processed 16261 tokens with 665 phrases; found: 556 phrases; correct: 104.\r\n",
      "accuracy:  91.26%; precision:  18.71%; recall:  15.64%; FB1:  17.04\r\n",
      "          company: precision:  26.09%; recall:  15.38%; FB1:  19.35  23\r\n",
      "         facility: precision:  14.89%; recall:  17.95%; FB1:  16.28  47\r\n",
      "          geo-loc: precision:  25.00%; recall:  24.14%; FB1:  24.56  112\r\n",
      "            movie: precision:   0.00%; recall:   0.00%; FB1:   0.00  6\r\n",
      "      musicartist: precision:   0.00%; recall:   0.00%; FB1:   0.00  58\r\n",
      "            other: precision:  13.54%; recall:   9.85%; FB1:  11.40  96\r\n",
      "           person: precision:  30.92%; recall:  27.49%; FB1:  29.10  152\r\n",
      "          product: precision:   3.85%; recall:   2.70%; FB1:   3.17  26\r\n",
      "       sportsteam: precision:   6.25%; recall:   2.86%; FB1:   3.92  32\r\n",
      "           tvshow: precision:   0.00%; recall:   0.00%; FB1:   0.00  4\r\n"
     ]
    }
   ],
   "source": [
    "! cat test.dynet.tsv | tr '\\t' ' ' | perl -ne '{chomp;s/\\r//g;print $_,\"\\n\";}' | python data/conlleval.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f66a4067550>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AFyrXV9xr1qypwMBAnTp1SoWFhSV+u8zZ97P+13ec8Udp77M+cOBAvl0GAAAAV6TatWtr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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f66a43dbf90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(TRANS_LOOKUP.as_array())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 'O',\n",
       " 1: 'U-geo-loc',\n",
       " 2: 'B-facility',\n",
       " 3: 'E-facility',\n",
       " 4: 'B-movie',\n",
       " 5: 'E-movie',\n",
       " 6: 'U-company',\n",
       " 7: 'U-product',\n",
       " 8: 'U-person',\n",
       " 9: 'B-other',\n",
       " 10: 'E-other',\n",
       " 11: 'B-sportsteam',\n",
       " 12: 'E-sportsteam',\n",
       " 13: 'I-other',\n",
       " 14: 'B-product',\n",
       " 15: 'I-product',\n",
       " 16: 'E-product',\n",
       " 17: 'B-company',\n",
       " 18: 'E-company',\n",
       " 19: 'B-person',\n",
       " 20: 'E-person',\n",
       " 21: 'B-geo-loc',\n",
       " 22: 'E-geo-loc',\n",
       " 23: 'U-other',\n",
       " 24: 'I-facility',\n",
       " 25: 'U-sportsteam',\n",
       " 26: 'U-tvshow',\n",
       " 27: 'B-musicartist',\n",
       " 28: 'E-musicartist',\n",
       " 29: 'U-facility',\n",
       " 30: 'I-musicartist',\n",
       " 31: 'B-tvshow',\n",
       " 32: 'E-tvshow',\n",
       " 33: 'I-person',\n",
       " 34: 'U-musicartist',\n",
       " 35: 'I-geo-loc',\n",
       " 36: 'I-company',\n",
       " 37: 'I-movie',\n",
       " 38: 'U-movie',\n",
       " 39: 'I-tvshow',\n",
       " 40: 'I-sportsteam',\n",
       " 41: '_S_'}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vt.i2w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42,)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_ids = np.array([vt.w2i[t] for t in sorted(vt.i2w.values(), key=lambda x: x[2:] + x[:2])])\n",
    "sorted_ids.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42, 42)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TRANS_LOOKUP.as_array()[sorted_ids][:, sorted_ids].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f6695c9cf10>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAABcEao13P/jP/5DixYt0rBhw9S1a1f9/ve/P++xTz75pI4fP66pU6dq+PDh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\n+D9GU26lZYmT2wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6695e0a790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(TRANS_LOOKUP.as_array()[sorted_ids][:, sorted_ids])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
